亚群体的得分分布如何影响公平观念?

Carmen Mazijn, J. Danckaert, V. Ginis
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引用次数: 1

摘要

基于训练算法的自动化决策对人类生活的影响越来越深远。近年来,很明显,这些决定往往伴随着对不同亚群体的偏见和不公平待遇。与此同时,科学文献中流传着一些公平的概念,在利润与公平之间以及它们之间的公平指标之间进行权衡。基于分析计算和数值模拟,我们在本研究中表明,一些利润-公平权衡和公平-公平权衡在很大程度上取决于给予亚群体的潜在得分分布,我们提出了两个互补的观点来可视化这种影响。我们进一步表明,在给定的可接受严格度内,更高的子种群分数的对称性可以显著减少公平概念之间的权衡,即使牺牲了表达性。我们的探索性研究可能有助于理解如何克服关于某些公平概念的统计不相容的严格数学陈述。
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How Do the Score Distributions of Subpopulations Influence Fairness Notions?
Automated decisions based on trained algorithms influence human life in an increasingly far-reaching way. In recent years, it has become clear that these decisions are often accompanied by bias and unfair treatment of different subpopulations.Meanwhile, several notions of fairness circulate in the scientific literature, with trade-offs between profit and fairness and between fairness metrics among themselves. Based on both analytical calculations and numerical simulations, we show in this study that some profit-fairness trade-offs and fairness-fairness trade-offs depend substantially on the underlying score distributions given to subpopulations and we present two complementary perspectives to visualize this influence. We further show that higher symmetry in scores of subpopulations can significantly reduce the trade-offs between fairness notions within a given acceptable strictness, even when sacrificing expressiveness. Our exploratory study may help to understand how to overcome the strict mathematical statements about the statistical incompatibility of certain fairness notions.
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